摘要
粒子群优化(PSO)算法是根据鸟群觅食过程中的迁徙和群集模型而提出的用于解决优化问题的算法,是一类随机全局优化技术,它通过粒子间的相互作用搜索复杂空间中的最优区域,其优势在于效率高,且又简单易实现。笔者讨论了PSO算法用于板状体磁异常数据反演的方法,并与遗传算法(GA)进行了比较。理论和实测磁异常数据反演的结果表明,PSO算法具有更高的找寻最优解效率,是一种很有潜力的位场反演工具。
Particle swarm optimization （PSO）, based on the idea of a swarm of birds searching for foods, is a new global optimization scheme. It can find optimal regions in searching space through the interaction of individuals in a population of particles, with the ad- vantages of efficient searching and easy implementation. This paper deals with the inversion of the magnetic data of tabular bodies by means of PSO in comparison with the genetic algorithm （GA）. The inversion results of theoretical and practical data show that PSO can find the optimal solution with fairly high efficiency.
出处
《物探与化探》
CAS
CSCD
北大核心
2009年第2期194-198,共5页
Geophysical and Geochemical Exploration
关键词
PSO算法
GA算法
板状体磁异常
反演
particle swarm optimization
genetic algorithm
magnetic data of tabular bodies
inversion
